TipRanks

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide read-only, idempotent, non-destructive hints. The description does not add behavioral context beyond purpose, missing details like response format, rate limits, or data freshness.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, no redundancy, front-loaded with key information. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With zero parameters and an output schema, the description fully covers the tool's purpose and scope. No gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters exist, so schema coverage is 100%. Description adds no parameter info, but baseline 4 is appropriate given zero parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (returns real-time quotes) and the resource (all commodities) in a single request, distinguishing it from sibling tools like get_commodity_quote.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The purpose is clear and implicitly guides when to use this tool (for all commodities) vs alternatives (single commodity), but lacks explicit when-not scenarios or comparison with siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds behavioral context beyond annotations by specifying 'real-time' and 'all cryptocurrencies'. Annotations already cover readOnly and idempotent hints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, focused sentence that conveys the essential purpose without any unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present and no parameters, the description is fairly complete. It could mention that it returns a snapshot or list, but it's adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters exist, so baseline is 4. The description does not need to add parameter meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns real-time quotes for all cryptocurrencies, distinguishing it from the sibling 'get_crypto_quote' which likely returns a single quote.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for obtaining a broad overview of all crypto quotes but does not explicitly state when to use it versus alternatives like get_crypto_quote.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only and idempotent. Description adds 'real-time' and 'all forex pairs' context, enhancing transparency beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, no redundancy, front-loaded with key action and scope. Every word serves a purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no parameters and presence of output schema, description sufficiently informs the agent about tool behavior. It specifies what is returned and the scope.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters present; schema coverage is 100%. Baseline score of 4 for no parameters, description does not need to add param details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it returns real-time quotes for all forex pairs. Verb is specific and resource is well-defined, distinguishing it from siblings like get_forex_quote which likely returns a single pair.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implied usage: when all forex pairs are needed, use this tool. However, no explicit exclusion for single-pair queries; siblings provide context but description lacks direct guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond annotations, the description discloses important behaviors: deduplication by analyst name keeping best rank, sorting by rank, and conditional shape change when sector is provided. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured, with a front-loaded purpose, clear bullet-point-like output format, and separate Args section. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema, the description need not detail return values but still provides a clear overview of row structure and conditional changes. It covers all necessary aspects for a list tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by explaining both parameters: firm with substring match example, and optional sector with its effect on output shape.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists analysts at a given firm with a specific verb and resource, and distinguishes itself from sibling tools by its specific function and output format.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context on when to use the tool (to list analysts by firm with case-insensitive substring match) and implies not to use it for other purposes, though it does not explicitly list alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare safe, idempotent, non-destructive. The description adds rich behavioral details: HTML-stripped body capped at 8000 chars, return fields (title, excerpt, author, etc.), and error handling for missing articles. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with no unnecessary words. It front-loads the purpose, then details return values and arguments. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple single-parameter tool with robust annotations and output schema (implied by return description), the description covers all necessary aspects: input format, output contents, character limit, and error case. It is fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description already covers the identifier parameter well (numeric id, slug, full URL). The tool description repeats this info and adds context about origin, but does not significantly extend meaning beyond schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Fetch the full text of a single TipRanks article' and elaborates on resolving identifiers from URLs, slugs, or numeric IDs. It distinguishes from sibling tools like get_latest_news by specifying single-article retrieval with full content.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description suggests using identifiers from get_latest_news or get_assets_news, guiding when to use this tool. It lacks explicit when-not-to-use or alternatives, but the context is clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral specifics: output format (JSON with assetsData array), rounding of floats, day-resolution dates, and a note to quote daysUntilEarnings rather than compute it. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured, starting with a clear one-sentence summary, then detailing input format, output fields, and alternatives. While somewhat verbose due to detailed field explanations, it remains front-loaded and organized. A slightly more concise description would earn a 5.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has one parameter and an output schema (not shown but present), the description thoroughly covers input format, output structure, field semantics, and alternative tools. It is complete enough for an agent to select and invoke this tool correctly among over 50 siblings.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage for the single parameter 'tickers'. The description compensates fully by explaining the accepted formats: a comma-separated string or a JSON array. This adds significant value beyond the bare schema type definition.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns key TipRanks stock data for one or more tickers, with a specific verb and resource. It distinguishes itself from sibling tools by noting that deeper detail is available in dedicated tools, establishing its role as a headline summary.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance: 'This is a headline summary tuned to stay small enough for a whole portfolio to fit in a single response.' It also lists alternative tools for deeper detail (get_stock_prices, get_hedge_fund_holdings, etc.), providing clear when-to-use and when-not-to-use advice.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds no behavioral traits beyond what annotations provide, such as rate limits or data freshness. It does not contradict annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise: two sentences plus parameter docs. Every sentence adds value, and the purpose is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the existence of an output schema, the description adequately states what is returned (dividend, earnings, coupon dates per ticker). It could mention date range behavior when dates are omitted, but overall sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description compensates by explaining the tickers parameter (comma-separated format) and date parameters (ISO format). It provides enough context to use the parameters correctly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns upcoming dividend and earnings events for tickers, specifying the exact resource and action. It distinguishes itself from sibling tools like get_dividend_history or get_earnings_calendar by focusing on both event types and coupon dates.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains the parameters and their formats, but does not provide explicit guidance on when to use this tool versus alternatives. For example, it doesn't mention that this tool is suitable for combined dividend and earnings events rather than separate queries.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds valuable context: returns only headlines, sentiment classification from TipRanks, redundant date fields with preference for publishTime, and clarification that from_date filters on the date field. It does not explicitly mention the openWorldHint (non-exhaustive results), but the transparency is strong overall.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a summary paragraph, Args section, and Returns section. It is not overly long, and every sentence adds value. The explanation of redundant date fields is slightly verbose but useful. Front-loading is effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists, the description still provides detailed field explanations, including sentiment bucket list and date field preferences. It covers the key behavior (headlines only, date filtering) and sibling distinction. Missing details about error cases or result limits, but overall complete for a 3-param read-only tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is low (33% for from_date), but the description compensates by explaining tickers as 'comma-separated ticker symbols' and count as 'Number of articles to return (default 10)'. For from_date, it adds 'filtered on date' beyond the schema. This adds meaningful guidance for an AI agent.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns recent news articles for tickers with sentiment and source URL, and explicitly distinguishes from get_latest_news for general or excerpt news. The verb 'Returns' and resource 'news articles for tickers' are specific and clear.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use get_latest_news instead (for general/market news or article excerpts). It also sets expectations by noting 'headlines only — no article body'. However, it does not cover when to use other sibling news tools like get_crypto_news, though that may be implied by ticker type.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description adds value by specifying return fields (warning type, date, detail) and parameter format. It does not disclose limits or error cases, but this is acceptable for a simple read tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with two sentences and structured Args/Returns lines. It is front-loaded with the main purpose and contains no unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool with an output schema, the description covers purpose, parameter format, and return content. It is sufficiently complete for an agent to select and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has no descriptions (0% coverage), so the description compensates by explaining that 'tickers' should be comma-separated symbols and describes the return fields. This adds meaningful guidance beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns risk warnings for tickers, using verb 'returns' and resource 'risk warnings'. The name and title reinforce this, and it distinguishes from sibling tools like 'get_assets_data' which return different asset information.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. Usage is implied from the purpose, but it does not mention when not to use it or contrast with other get_assets_* tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds ranking criteria (success rate and average return) but does not disclose additional behavioral traits like data freshness or rate limits, so added value is moderate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise: two sentences plus parameter list. Purpose is front-loaded. Could be slightly more structured, but no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given two parameters, no required fields, and an output schema, the description adequately explains the tool's purpose and constraints. It doesn't cover all edge cases but is sufficient for typical use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 50% and schema already describes expert_type. The description adds the 'max: 25' constraint for num, which is not in the schema, but otherwise repeats schema info. Overall adds some but not substantial meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it returns the best-performing experts, ranked by success rate and average return. This distinguishes it from sibling tools like get_analysts_by_firm or get_top_hedge_fund_managers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Description explains parameters and defaults but does not explicitly state when to use this tool vs. alternatives. Agents must infer usage from the purpose, which may be ambiguous among similar performance-related tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds value by detailing the return structure (JSON with fields like ticker, updatedOn, bullish, bearish, key_points) and the 'tagged with a topic' aspect. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with sections for Args and Returns, and uses bullet points for the return fields. It is clear and informative, though slightly verbose with the 'key_points' list description. Could be slightly more concise but overall efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that the tool has an output schema (though not fully shown), the description explains the return structure thoroughly. The single parameter is well-documented. The tool is simple and the description provides all necessary context for an agent to use it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With schema description coverage at 0%, the description compensates by explaining that tickers should be 'Comma-separated tickers (e.g. 'AAPL,TSLA')'. This adds meaning beyond the schema's minimal type info. The description also clarifies that the parameter can be a string or array.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns 'discrete bull and bear key points for stocks' with individual sentence bullets tagged with a topic. It uses specific verbs ('Returns', 'tagged') and identifies the resource ('stock pages'). It distinguishes from numerous sibling tools by being stock-specific and sentiment-focused.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context on when to use the tool (to get bull/bear key points for stocks). It includes a parameter explanation and return format. However, it does not explicitly state when not to use or name alternatives among the many sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds behavioral details like sorting order, possible duplicate entries, and pagination behavior, enriching transparency beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with sections, bullet format, and front-loaded key info. Every sentence adds value with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema is provided and the tool has only 3 simple parameters, the description covers return structures for both modes comprehensively, making it fully informative.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite low schema coverage (33%), the description thoroughly explains each parameter: ticker optional, limit and page with defaults and max, and conditional usage per mode. It adds significant meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Returns share-buyback / stock-repurchase activity' and distinguishes two modes (cross-market and per-ticker), using specific verbs and resource. It stands out from sibling tools by focusing on buyback data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit context for each mode: cross-market for 'biggest buybacks', per-ticker for historical series. Does not explicitly state when not to use, but the guidance is sufficient for correct selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds fixed time window, sorting, and return format, enriching behavioral context without contradicting annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences: purpose, window/detail, use case. Every sentence adds value, no fluff. Front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero parameters, good annotations, and a described output schema, the description fully covers what the agent needs to know: purpose, fixed window, return structure, and use case.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters (schema coverage 100%). Baseline 4 for zero parameters; description adds no param detail, but none needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Describes a specific verb-resource pair: 'Returns the FDA / clinical-trial calendar' with upcoming drug approval target dates and tickers. Clearly distinguishes from siblings which are about commodities, crypto, forex, etc.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states 'Use for upcoming FDA decisions, PDUFA dates, biotech catalysts,' providing clear context. No explicit when-not, but the domain is narrow enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and idempotentHint. Description adds context about daily frequency and ordering, but does not detail rate limits or pagination. No contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences plus a parameter list. No redundant information; every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema (true), the description covers the tool's purpose, return structure, and parameter usage completely for a simple historical data tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 33% (only symbol has description). Description adds examples and date format for all parameters, compensating for schema gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it provides 'Daily OHLCV time series for a commodity' and specifies the fields returned. It distinguishes itself from sibling tools like get_commodity_quote (single quote) and get_crypto_historical (crypto-specific).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Description implies use for historical price data and mentions 'Chart-ready' for direct plotting, but does not explicitly state when not to use or compare with alternative tools among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint, idempotentHint, etc. Description adds 'real-time' nature, which is mildly informative but does not contradict or significantly expand beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise (two short paragraphs), front-loaded with primary action, and well-organized with examples in a clean list.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given output schema exists and tool is simple (one parameter), the description sufficiently covers purpose and symbol examples. No gaps for a read-only query of a single commodity quote.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% coverage; description lists common symbol examples but adds minimal meaning beyond the schema's description. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'returns the real-time price quote for a specific commodity' with common symbol examples. This distinguishes it from siblings like get_all_commodities_quotes (bulk) and get_crypto_quote (different asset class).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage context is implied: use for a single commodity quote. However, no explicit when-to-use/not-to-use guidance or comparison with alternatives (e.g., get_all_commodities_quotes) is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, etc. Description adds critical context: full history is large, default returns limited periods, and parameter effects (count cap, group filtering). No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is structured with arguments and return format, but slightly verbose. Could be more concise, but still clear and front-loaded with purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no explicit output schema provided, the description fully specifies the return JSON structure (ticker, lastUpdated, groups with slugs, titles, series). Covers all necessary context for tool use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 33%, but description compensates by detailing 'count' (default 8, max 40, 0 for full) and 'group' (optional slug or title). Adds meaning beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly specifies the tool returns company-specific operational KPIs with examples (segment revenue, geographic breakdown, etc.), and distinguishes from many sibling tools that focus on other data types.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explains default behavior (most recent count periods), how to get full history (count=0), and how to fetch a single group. No explicit when-not-to-use, but context makes it clear this is for company KPIs.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and idempotentHint, so the description adds valuable context: data is ordered for direct plotting and chart-ready. This goes beyond the structured annotations. No contradiction found.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (two short paragraphs) with no redundant information. Each sentence serves a purpose: stating output format, then listing parameters. Front-loaded with the core functionality.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that an output schema exists (implied by 'Has output schema: true'), the description adequately covers purpose, parameters, and output fields. It is complete for a historical data tool, leaving no major gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 33% (only symbol has a brief description), but the description adds detailed parameter explanations: symbol example, date format YYYY-MM-DD, and default empty strings. This compensates for the low schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description specifically states it provides 'Daily OHLCV time series for a cryptocurrency' and describes the output as 'chart-ready' rows with date, open, high, low, close, volume. This clearly distinguishes it from sibling tools like get_crypto_quote (current price) or get_crypto_news.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context: it gives historical data with optional date parameters. However, it does not explicitly exclude use cases or contrast with alternatives like get_crypto_quote or get_all_crypto_quotes, which slightly reduces guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. Description adds no behavioral details beyond output format (headlines, snippets, URLs). No mention of pagination behavior, rate limits, or response size.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is very short and front-loaded with one sentence and an Args block. Efficient, though the Args section is redundant with the schema. No fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists, description need not detail return values. However, it lacks information about number of articles per page, max pages, or any filtering. Adequate for a simple list but could be more complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%; description adds basic meaning for 'page' as pagination parameter with default 0. However, it does not explain page size or total results. Adequate but minimal.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it returns latest cryptocurrency news with headlines, snippets, and URLs. The specific mention of 'cryptocurrency' distinguishes it from general news tools, but does not address similarities with siblings like get_assets_news.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool vs. alternatives such as get_latest_news or get_assets_news. The description lacks any usage context or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds 'real-time' but no additional behavioral context such as data freshness, rate limits, or fallback behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise: one sentence for purpose, then a clear list of common symbols, and a docstring-style argument explanation. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity, the description covers the main functionality. However, it lacks details on case sensitivity, error handling, or whether the output includes bid/ask prices, which the output schema may already provide.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by listing common symbol examples (BTCUSD, ETHUSD, etc.), which helps the agent use correct values.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns the real-time price quote for a specific cryptocurrency with a specific verb and resource. It lists common symbols, distinguishing it from siblings like get_commodity_quote or get_stock_quotes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for getting a single crypto quote, but does not explicitly state when to use this tool over alternatives like get_all_crypto_quotes or how to determine the correct symbol.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only, idempotent, non-destructive. The description adds critical details: yield as percentages, ordering (newest first), cap on per-payment list, full annual totals. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with bullet-like format and front-loaded purpose. Each sentence adds value, though slightly verbose. Could be tightened without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (two time series, snapshot) and presence of output schema, the description covers all necessary aspects: data structure, yield encoding, use cases, and parameter limits. Complete for agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description's 'Args' section explains both parameters (ticker and count) with examples and constraints (max 100 for count). Adds meaning beyond schema defaults and types.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns 'Dividend payment history for a ticker' with detailed output structure. It specifies chart-ready data including current snapshot and two time series, distinguishing it from other market data tools like stock prices or earnings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit use cases like 'AAPL dividend history' and 'yield trend over 5 years', but does not directly compare to sibling tools or specify when not to use it. The examples strongly imply appropriate contexts.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and idempotentHint, covering safety. Description adds date-range behavior but lacks details on pagination, error handling, or result structure. Adequate but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Everything is front-loaded: purpose statement followed by clean parameter list. No redundant information. Highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given low complexity, strong annotations (readOnly, idempotent), and presence of output schema, the description covers all necessary aspects. No need to repeat return values per rules.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description compensates by documenting each parameter's purpose, format (YYYY-MM-DD), and defaults. However, it omits validation constraints like allowed date range or exchange values.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it returns upcoming earnings dates across all stocks for a date range, with specific verb and resource. Differentiates from siblings like get_ticker_earnings (specific ticker) and get_earnings_history (historical).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides defaults and optional exchange filter, implying use for broad date-range queries. No explicit when-not vs alternatives, but the name and context among siblings are clear enough to infer appropriate usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds that only the most recent quarter is stored per ticker, which is useful operational detail. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is informative with a clear structure: function statement, brief content, use cases, alternative, then parameter and return docs. Slightly verbose but every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has one simple parameter and an output schema (not shown but indicated), the description covers purpose, parameter, return format, and comparison to sibling. No gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has zero description coverage for the 'ticker' parameter. Description explicitly defines it as 'Stock ticker (e.g. 'AAPL', 'NVDA')', adding necessary meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description specifies the tool returns 'the most recent earnings call summary for a ticker' including guidance, sentiment, and highlights/lowlights. It clearly distinguishes itself from get_earnings_history by noting the latter is for historical EPS actual-vs-estimate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit use cases: 'what did management say about X on the last call', 'was the most recent call bullish or bearish', 'summarise the highlights'. Directs users to get_earnings_history for historical EPS data, offering clear when-to-use and when-not-to guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint, idempotentHint, destructiveHint=false. Description adds behavioral details: data is ordered oldest-first, returns next_quarter with consensus estimate, low/high band, and expected report date. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is well-structured and concise: starts with core purpose, then data format, then next_quarter, then example queries, then parameters. No fluff; each sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema (not shown), the description does not need to detail return values. It already explains row contents and next_quarter info. For a 2-param tool with good annotations, it is fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but description explains both parameters: ticker (e.g., 'AAPL'), quarters (default 8, max 40). This adds meaning beyond the schema, which only specifies type and default.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it returns quarterly earnings time series for a ticker, with chart-ready data and next_quarter info. It includes specific example queries like 'AAPL earnings history' and 'did NVDA beat last quarter', distinguishing it from sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Description provides concrete use cases (e.g., 'earnings surprise trend', 'EPS beat/miss the past 4 quarters') that guide the agent on when to use this tool. However, it does not explicitly state when not to use it or compare with alternatives like get_ticker_earnings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate safe read-only operations. The description adds behavioral details: default filtering logic, parameter limits (max 200), and return structure. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise yet complete, starting with a clear purpose, followed by a rationale for defaults, then a well-organized parameter list. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 7 optional parameters, solid annotations, and an output schema, the description covers all necessary context: filtering behavior, return format, and usage examples. It does not replicate output schema details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With only 29% schema coverage, the description compensates fully by explaining each parameter's purpose, default values, and special behaviors (e.g., empty string to disable filters). Examples for countries and impact levels are provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns upcoming and recent economic events, listing specific examples like Fed meetings, jobs reports, CPI, GDP. It distinguishes from sibling tools like get_earnings_calendar by specifying economic calendar scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use defaults and how to customize via parameters. It notes the rationale for filtering US High/Medium impact items. However, it does not explicitly compare to sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the tool is clearly read-only and safe. The description adds value by explaining the specific calculation method (weighted average of holdings' ratings), which goes beyond annotations. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: two sentences plus a parameter explanation. Every sentence is meaningful and front-loaded with the core purpose. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with only one parameter and an output schema (assumed present), the description covers the purpose, parameter format, and computation method. No gaps remain for an agent to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% schema description coverage, so the description must compensate. It clearly explains the 'tickers' parameter: 'Comma-separated ETF tickers (e.g., 'QQQ,SPY,VTI')', providing format and example. This adds substantial meaning beyond the schema definition.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it 'Returns analyst consensus for ETFs based on weighted average of holdings' ratings.' This specifies the verb (returns), resource (analyst consensus for ETFs), and methodology (weighted average of holdings' ratings), distinguishing it from sibling tools like get_etf_holdings or get_etf_exposures.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit when-to-use or when-not-to-use guidance is provided. The description implies usage for getting ETF analyst consensus but doesn't contrast with alternatives or provide usage boundaries. Among many sibling tools, clear differentiation is left to the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds significant behavioral context beyond annotations: sorting order by holding market value, pagination behavior, and detailed return fields. Annotations already provide readOnlyHint, idempotentHint, etc., and the description complements them without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear first sentence, an Args block, and a Returns block. It is concise yet comprehensive, with no unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no explicit output schema provided, the description enumerates all return fields in detail. For a paginated list tool, this covers all needed information for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, meaning the description carries all parameter information. It explains each parameter: ticker with examples, page defaults, page_size with default and max. This adds substantial meaning beyond the schema's type/defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns the list of ETFs holding a specific stock, sorted by market value. The verb 'Returns' and resource 'list of ETFs holding a stock' are specific, and it distinguishes from sibling tools like get_etf_holdings (ETFs' holdings) and get_etf_top_stocks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description conveys the tool's purpose clearly but does not explicitly state when to use it versus alternatives or provide exclusions. The context from sibling names helps, but direct guidance is absent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint. The description adds no extra behavioral context beyond what annotations provide, but does not contradict them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise, with two sentences and an Args line. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read-only tool with one parameter and an output schema, the description covers the essential purpose and parameter. It does not explain output format, but that is covered by the output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema coverage, the description compensates by clearly stating the ticker parameter is an ETF ticker and providing an example ('QQQ').

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns analyst forecasts for a specific ETF, listing consensus, price target, and upside. It distinguishes from siblings like get_etf_holdings and get_etf_exposures.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when an ETF forecast is needed but provides no explicit guidance on when to use versus alternatives or any prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnly, openWorld, idempotent, non-destructive. Description adds concrete output fields per mode and explains behavior of sort_by and limit, providing useful context beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear sections for modes and parameter descriptions. Slightly verbose but every sentence adds value; no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers inputs, mode selection, and output fields. With an output schema present, return values are handled. Missing edge cases like error handling or ticker validity, but sufficient for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage only 33%, but description thoroughly documents all three parameters: ticker example, sort_by options, limit behavior. Adds meaning beyond the schema, especially for sort_by enumeration and the distinction between modes.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it lists ETF constituents with two modes (base and top-performers). Distinguishes from sibling tools like get_etf_top_stocks by specifying it returns individual holdings with weights.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly recommends top-performers mode for specific questions like 'which stocks in XLK rose most this week', but does not explicitly state when not to use or alternatives beyond implication.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and no destructiveness. The description adds the specific filter values but does not disclose behavioral traits beyond those annotations (e.g., result ordering, pagination, response shape). It is adequate but not enhanced.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: one sentence plus a parameter list. It front-loads the primary action and then lists arguments. There is no extraneous text, and every element contributes to understanding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema (and annotations for safety), the description sufficiently explains the tool's purpose and parameters. It does not detail the return format, but the output schema covers that. Minor gap: no mention of the result being a list or any filtering logic when multiple parameters are combined.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description lists example values for each parameter, which mirrors the schema descriptions. It adds marginal readability but no new semantic meaning beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action ('Filters ETFs') and the three filtering dimensions (asset class, category, focus). It distinguishes from sibling ETF tools (e.g., get_etf_holdings) implicitly by its screening nature, but does not explicitly contrast. The verb and resource are specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives like get_etf_analysis or get_etf_top_stocks. There is no mention of prerequisites, default behavior when no filters are provided, or when not to use. The agent must infer usage from the name and schema.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, destructiveHint, idempotentHint. Description adds context that it includes analyst ratings, but does not explain what 'top' means (e.g., by weight) or any limits. Bar is lowered due to annotations, but missing details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: first states purpose, second describes param. No wasted words, front-loaded information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one parameter and an output schema, the description is mostly complete. Missing minor detail on how 'top' is determined, but overall adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only one parameter 'ticker' with an example 'QQQ' provided. With 0% schema description coverage, the description compensates well by explaining the argument clearly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it returns top stock holdings within an ETF along with analyst ratings. Distinguishes from siblings like get_etf_holdings (all holdings) and get_etf_analysis (analysis, not holdings with ratings).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implied usage: when you need top holdings and ratings for an ETF. No explicit when-to-use or when-not-to-use, and no comparison to sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate read-only, idempotent, non-destructive behavior. The description adds valuable conventions: margins as percentages, cash outflows as negative numbers, and ordering of periods. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear sections and bullet points. Slightly verbose but every sentence adds information. Could be more concise without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers all essential aspects: input parameters, output format (flat object fields), conventions, and example use cases. An output schema exists, but description still provides helpful context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 33% (period described). The description explains ticker, period (with options and extent), and count (default, max, ordering) in detail, adding significant value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description specifies the tool provides financial statements time series for a ticker, chart-ready, with detailed field breakdown across statements and ratios. It lists example queries, clearly distinguishing it from siblings like get_stock_quotes or get_earnings_calendar.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes 'Use for:' with concrete examples, effectively guiding when to use the tool. It does not explicitly state when not to use or mention alternatives, but the examples provide clear context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false, indicating safe read-only behavior. The description adds value by stating that output rows are ordered for direct plotting and include specific fields (date, open, high, low, close, volume), which goes beyond the annotations. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is reasonably concise but includes a bulleted args section that repeats some schema information. It is front-loaded with the key purpose, but the args list could be integrated more succinctly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema (not shown but referenced), the description covers the core functionality and return format adequately. It does not discuss error handling, rate limits, or data coverage limits, but these are not critical for a simple historical data retrieval tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is only 33% (only symbol has a description), but the description lists all three parameters with brief explanations, including format hints (e.g., 'YYYY-MM-DD' for dates). This adds some meaning beyond the schema, though details on date range constraints or default behavior are missing.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides daily OHLCV time series for a forex pair, specifying it is 'chart-ready' with fields ordered for direct plotting. This distinguishes it from sibling tools like get_forex_quote (current quote) and get_all_forex_quotes (list of pairs), as well as historical tools for other asset classes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for fetching historical forex data via the args section (symbol, optional date range), but does not explicitly contrast with alternatives like get_forex_quote or get_all_forex_quotes. No when-not-to-use or prerequisite information is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true. The description adds 'real-time' which aligns with openWorldHint. No contradictions or additional behavioral context beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and front-loaded with the main purpose. The 'Args' section is redundant with the schema but structured clearly. Removing the redundant lines would improve conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simplicity of the tool (single required parameter, annotations covering safety, output schema existing), the description provides sufficient information for correct usage. No missing essential details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description repeats the parameter info and provides example values, which adds marginal semantic value. However, it does not clarify any constraints beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns real-time exchange rate quotes for a single forex pair, with examples. However, it does not differentiate from the sibling 'get_all_forex_quotes' which likely returns quotes for multiple pairs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the description: use for a single real-time quote. No explicit when-to-use or alternatives compared to other forex tools like 'get_forex_historical' or 'get_all_forex_quotes'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows it's a safe read operation. The description adds value by detailing the returned JSON structure including fields like rank, stars, and is_active, going beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: a one-line summary, a sibling comparison, then Args and Returns sections. Every sentence provides useful information without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (2 parameters, output schema present), the description is fully complete. It explains the tool's purpose, relationship to alternatives, parameter details, and return format, leaving no gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% coverage (no descriptions for parameters), but the description compensates by explicitly documenting both parameters: 'ticker' and 'limit' with default and max values. This adds necessary meaning beyond the raw schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns per-fund hedge fund holdings and recent transactions for a ticker, listing specific fields. It explicitly distinguishes itself from the sibling tool 'get_hedge_fund_holdings' by contrasting it as the aggregate share-count timeseries.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context by mentioning 'Complements get_hedge_fund_holdings', implying when to use each tool. It also specifies defaults and max for the limit parameter. However, it does not explicitly state when not to use this tool or list other alternatives beyond the one sibling.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. Description adds behavioral context: returns up to 60 snapshots, includes summary block, source from quarterly 13F filings with up to 45-day lag. This goes beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise with no wasted words. Structured as purpose, details, alternative guidance, then parameter descriptions. Front-loaded with core function.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given tool complexity (2 parameters) and presence of output schema, description covers return format, source, lag, limit behavior, and alternative sibling tool. No gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has no descriptions (0% coverage), but description's Args section explains ticker and limit, including default and max value. Fully compensates for lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns 'Hedge-fund share-count history for a ticker' with details on snapshots and summary block. It distinguishes from get_assets_data, which provides current sentiment without timeseries.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use this tool vs get_assets_data: 'For a snapshot of current sentiment without the timeseries, use get_assets_data.' Also mentions data lags and max limit constraints.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint: true, openWorldHint: true, idempotentHint: true, destructiveHint: false, indicating safe behavior. The description adds that data is chart-ready and ordered for plotting, which is useful but does not disclose potential rate limits or other behavioral traits. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with a clear two-paragraph structure: first stating purpose and output format, then listing parameters. Every sentence adds value, no redundancy. Front-loaded with main purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema (not shown but indicated), the description does not need to explain return values. It covers the essential purpose, parameter details, and data format. Sibling tools are provided for context. The description is complete for an agent to understand and invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is low (33%), with only the 'symbol' parameter having a description. The tool description compensates by detailing parameter formats (YYYY-MM-DD for dates), default for 'to_date', and a concrete symbol example ('^GSPC'), adding meaningful context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it provides 'Daily OHLCV time series for a major market index' and specifies the data format as chart-ready with columns date, open, high, low, close, volume. This distinguishes it from sibling tools like get_index_quote (current quote) and other historical tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly state when to use this tool versus alternatives. While the context implies it is for historical index data, there is no mention of related quote tools or selection criteria. However, the name and sibling list provide implicit guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and idempotentHint; description adds value by specifying real-time nature and listing which indices are available, enhancing transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise and front-loaded, clearly stating the action and then listing indices in a structured format without any wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema shown, the description fully covers all needed context: what it returns (real-time quote), valid symbols, and parameter format. Output schema exists to explain return values.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 100% coverage for the symbol parameter, but description adds a comprehensive list of valid index symbols with examples, significantly aiding correct invocation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it returns real-time quote for a major market index, lists supported indices, and distinguishes from sibling tools like get_stock_quotes or get_commodity_quote.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Description provides clear context for when to use (getting real-time index quotes) and lists valid symbols, but does not explicitly exclude cases or mention alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds crucial detail: price is approximate (value/shares, no exact price from source), and each row's fields are listed. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise and well-structured. Front-loads purpose, uses bullet list for output fields, no unnecessary text. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite many sibling tools, the description fully covers what the tool returns, edge cases (approximate price), and alternative tools. Complete for its complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but description explains limit default (30) and maximum (100) beyond schema. Also clearly states ticker is required. Adds meaningful context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states it retrieves individual corporate-insider transactions (Form 4) for a ticker, sorted newest first. It clearly distinguishes from retail sentiment tools like get_assets_data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit guidance: for retail/individual investor sentiment, use get_assets_data and read investorActivity. This specifies when not to use this tool and offers alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description goes beyond by detailing the full nested JSON structure, explaining field semantics (e.g., investorScore meaning, divergence signal between crowd and best investors), and noting edge cases like empty age-bucketed lists. This adds substantial behavioral context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is lengthy but well-structured: starts with a one-sentence purpose, then systematically breaks down the return JSON with bullet points and nested fields. Every part is informative, though some redundancy could be trimmed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite one input parameter, the tool returns complex nested data. The description covers all top-level keys and their fields, provides interpretive hints (e.g., the divergence signal), and notes practical behavior (shorter lists for tenure buckets). Given the output complexity, the description is thorough and complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With only one parameter (ticker) and 0% schema description coverage, the description compensates by providing a clear example ('e.g. NVDA') and context. This adds meaning beyond the bare schema definition.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns crowd/retail investor sentiment for a stock, with a specific verb ('Returns') and resource ('crowd/retail investor sentiment'). It uniquely distinguishes from siblings like get_stock_quotes or get_technical_analysis by focusing on sentiment and detailed portfolio-level data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide any guidance on when to use this tool versus alternatives (e.g., when to prefer over get_analyst_ratings or get_blogger_sentiment). No explicit when-not or context is given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and no destructiveness. The description adds date range behavior with defaults, but doesn't detail additional traits like output format or pagination. Provides some value beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise with a docstring-style parameter list. It front-loads the purpose and then explains parameters. Slightly verbose for a simple tool, but no wasted sentences.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero required parameters, existing output schema, and annotations covering safety, the description is reasonably complete. It covers purpose and parameter formatting. Missing usage guidelines but acceptable for a simple read-only tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so description carries burden. It adds meaning by specifying ISO format and default values for both parameters, which the schema omits. Could be improved with examples or constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns upcoming and recent IPOs. It uses a specific verb 'Returns' and resource 'IPOs', distinguishing it from sibling tools which cover different financial data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. The description implies date range usage through parameter defaults, but lacks when-not-to-use or context for selecting IPOs over other calendars.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, openWorld. Description adds that results are newest first, from specific feed, each with excerpt, and explains parameter effects. No contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose, then usage, then parameter details, then return format. Efficient two paragraphs, but could be slightly more structured (e.g., bullet points for params).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers all aspects: purpose, usage, parameters, return format, and links to get_article for full text. Good annotations and output schema complement. No gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers only 25% of parameters. Description compensates by explaining all four parameters: tickers (optional comma-separated), category (optional with enum via schema), from_date (date format), limit (default 20). Adds meaning beyond defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it retrieves latest TipRanks news articles from editorial/wire feed, newest first, with excerpts. Explicitly differentiates from sibling get_assets_news.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Describes use cases: general market news, news on specific stocks with summaries, browsing a category. Implicitly covers when not to use by contrasting with get_assets_news and directing to get_article for full text. Could be more explicit about exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark it as read-only, idempotent, and non-destructive. The description adds valuable context: it's cached, refreshed every ~4 hours, AI-generated, and not authoritative. This goes beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise (4 sentences), front-loaded with the core purpose, and every sentence adds necessary information without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero parameters and presence of output schema, the description fully covers the tool's behavior: cache freshness, return format, fallback, and usage caveats. No gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters exist, so schema description coverage is 100%. Baseline score of 4 is appropriate since no additional param info is needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it returns a cached equities market sentiment snapshot with specific fields (overallSentiment, atmosphere, etc.). It distinguishes itself from sibling tools like get_market_movers or get_investor_sentiment by focusing on aggregated AI-generated commentary.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Description explains it reads cache only and does not trigger regeneration, and returns unavailable if no cache. This clarifies when it's appropriate to use, though it doesn't explicitly state when not to use or provide alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds context about what the tool returns (gainers, losers, active) and explains category/country codes, which is beyond annotations. No contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with a one-sentence purpose and an Args list. However, the Args are somewhat unstructured (plain text instead of bulleted or formatted). Still effective and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With output schema present and annotations, the description covers the primary purpose, parameters, and some behavioral context. It does not detail return structure or sorting, but output schema likely covers that. Adequate for a market movers tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 67% (category and country have descriptions, num does not). The description adds an 'Args' section that explains all three parameters, including num, which schema does not. This adds significant value beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Returns today's market movers: biggest gainers, losers, most active.' which is a specific verb and resource. Among siblings, it is distinct from other quote or performance tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for market movers data but does not explicitly state when to use this tool vs alternatives like get_market_performance or get_trending_stocks. No when-not or alternative tool guidance is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds behavioral context beyond annotations by specifying output contents (sector breakdown, top/bottom performers), which helps the agent understand what the response includes. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is single paragraph, front-loaded with purpose, then lists parameters. No extraneous words. Could be slightly more structured (e.g., bullet list for args), but efficient and clear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given single parameter and existence of output schema, description covers the tool's purpose and primary input adequately. Does not explain return format or list possible outputs, but output schema may cover that. For a simple read tool, it is fairly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage (only type and default). Description compensates by listing explicit country code examples ('us', 'ca', 'gb', etc.), providing concrete usage guidance beyond the schema's default 'us'. This adds significant meaning for the agent.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'Returns market-wide performance for a specific country' with specific deliverables (average returns, sector breakdown, top/bottom performers). Verb 'returns' and resource 'market-wide performance' are explicit, distinguishing it from sibling tools like get_market_movers or get_sector_analysis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides list of country codes in arg description, which aids proper selection but does not explicitly state when to use this tool versus alternatives (e.g., get_market_movers for daily movers, get_sector_analysis for sector data). Usage context is implied but not delineated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses that quotes are typically delayed ~15 minutes and that Greeks and implied volatility are not available, advising not to fabricate them. This adds meaningful context beyond annotations (readOnlyHint, idempotentHint), and there is no contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a summary line, a column list, a behavioral note, and an Args section. It is informative but slightly lengthy due to the column list; however, every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema (not shown but noted), the description covers parameters and return columns adequately. With 6 parameters and 2 required, it provides sufficient context for an agent to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 33%, but the description provides an Args section that explains all parameters including ticker, expiration_date format, contract_type options, and filters. This compensates for low schema coverage, adding meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Contracts at one expiration, enriched with price + OI + volume', specifying the verb (get), resource (options chain), and scope (one expiration). It distinguishes from siblings like get_options_contract (single contract) and get_options_expirations (list of dates).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description guides usage: requires expiration_date from get_options_expirations, and explains contract_type options and optional filters. It does not explicitly state when not to use or list alternatives among siblings, but the context is clear enough for an agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by stating quotes are delayed ~15 minutes and Greeks/IV are unavailable, which are important behavioral traits beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is only 5 sentences, front-loaded with the core purpose, followed by essential details about ticker format and data limitations. No extraneous content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has an output schema and only one parameter, the description fully covers input format, data characteristics, and limitations. It is complete for its complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description adds a detailed explanation of the OCC ticker format (prefix, date, call/put, strike) and notes that the 'O:' prefix is optional, significantly enriching the parameter meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action ('Snapshot one OCC contract') and specifies the data returned (price, OI, intraday OHLCV, last trade), distinguishing it from siblings like get_options_chain or get_options_unusual_trades.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explain when to prefer this tool over alternatives or provide usage context. It only describes the ticker format and data limitations, leaving the agent to infer when to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds the behavioral detail that past expirations are excluded, which is not in annotations, enhancing transparency. No contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences: purpose, exclusion, usage guidance with parameter. Front-loaded with the primary action, no unnecessary words. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool with an output schema, the description is complete. It covers what it does, exclusions, and how to use the output. No need to detail return values due to output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate. It does so by documenting the single parameter 'ticker' with an example and clarifying it is the underlying ticker, adding significant meaning beyond the schema's title.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it lists future expiration dates for a ticker's listed options, with the verb 'List' and specific resource 'options expirations'. It distinguishes from sibling tools like get_options_chain by noting the result is used as input to that tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says to use the result as input to get_options_chain, providing clear usage context. It does not mention when not to use or give alternatives, but the guidance is sufficient for this simple tool among many options-related siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds context about selection criteria (size/sweep alerts) and return fields, enhancing transparency without contradicting annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise (3 sentences + bullet-style Args) with no wasted words. The first sentence front-loads the main purpose, followed by return details and usefulness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists (not shown but confirmed), the description covers the tool's purpose, input parameters, and return fields adequately. It does not explain how 'unusual' is determined beyond size/sweep alerts, but overall offers sufficient context for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by explaining both parameters: ticker as 'Underlying ticker' and limit as 'Max trades to return (default 25)'. The explanations are clear and sufficient.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Recent unusual option trades for an underlying (size/sweep alerts)' and details the returned fields. It distinguishes from siblings like get_options_chain, get_options_contract, and get_options_expirations by focusing on flagged trades, not full chains or single contracts.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use for spotting large bullish or bearish positioning via unusual trade alerts, but lacks explicit guidance on when to use this tool versus alternatives. No exclusions or when-not scenarios are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds limited behavioral context beyond the return type. No contradictions, but minimal added value.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise: two sentences plus an args section. Every word adds value, and the purpose is front-loaded. No wasted content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, the description adequately covers the tool's purpose and the single parameter. It does not need to explain return values, as the output schema handles that.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema coverage, the description provides a clear meaning for the ticker parameter with an example ('e.g. 'AAPL''), compensating well for the schema's lack of detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns peer companies with comparative metrics for a stock. It uses a specific verb and resource, and is distinct from sibling tools like get_stock_quotes or get_analysts_by_firm, though not explicitly differentiating.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool vs alternatives. It does not specify context, prerequisites, or exclusions, leaving the agent to infer usage solely from the name and description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, covering safety and idempotency. The description adds that it returns trades or holdings, but no other behavioral context is needed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise with two lines plus argument details, no wasted words. Front-loaded with purpose, then prerequisite, then parameter details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the annotations and presence of an output schema (implied), the description provides sufficient context: purpose, prerequisite, and parameter semantics. It is complete enough for a read-only data retrieval tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and description adds value by specifying that politicianId comes from get_politicians and activity is limited to 'trades' or 'holdings', enhancing the schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns 'a specific politician's stock trades or holdings'. It is distinct from sibling tools like get_politicians and others, as it focuses on a specific politician's activity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly instructs to first call get_politicians to obtain the politicianId, providing a clear prerequisite. It does not include when-not to use or alternatives, but the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, which fully convey the behavioral safety profile. The description adds no additional behavioral context beyond listing return fields, which is already covered by the output schema. The description does not contradict annotations but fails to add value beyond them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.